Abstract
Objective
We used de-identified clinical data from multiple healthcare systems using different electronic health records (EHRs) to 1) quantify the prevalence of common pediatric chronic diseases, 2) investigate patent characteristics associated with common pediatric chronic diseases, and 3) compare the results of our methodology to determine chronic disease prevalence with traditional approaches.
Materials and methods
We used a HIPAA-compliant and de-identified web application (Explorys; IBM Watson Health Explorys Inc.) to identify patients 17 years old or younger from multiple healthcare systems in the US who were seen in primary care clinics between 2016–2018 to determine the most common chronic conditions in this age group. The prevalence of chronic conditions was compared between different age groups, genders, races/ethnicities, and insurance; odds ratios and confidence intervals were calculated and reported.
Results
The top six chronic conditions identified by prevalence were: obesity/overweight (36.7%), eczema (15.8%), asthma (12.7%), food allergies (4.7%), attention deficit-hyperactivity disorder (4.09%) and hypertension (4.07%). Chronic conditions were generally more prevalent among relatively older pediatric patients, males, and African-American and multiracial groups.
Conclusions
Approximately 40% of children and adolescents have at least one chronic disease. Obesity/overweight, eczema, and asthma are the most common chronic diseases of childhood, in the US, among those seeking care in healthcare systems with EHRs. The work compiled herein demonstrates that aggregated, standardized, normalized and de-identified population-level EHR data can provide both sufficient insight and statistical power to calculate the prevalence of chronic diseases and the odds ratio of various risk factors.
BACKGROUND AND SIGNIFICANCE
Chronic conditions being defined by the US Centers for Disease Control and Prevention (CDC) as “conditions that last 1 year or more and require ongoing medical attention or limit activities of daily living or both”.1 Over the last century in pediatrics in the United States, the main burden of diseases has shifted away from infectious diseases, mainly acute diseases, and increasingly to chronic conditions.2 Improved neonatal and pediatric care has led to a higher survival rate for children leading into adulthood.3,4 Previous studies have noted that 13–27% of children are affected by chronic conditions.5 Chronic conditions affect many aspects of the lives of children with consequences that endure into adulthood.6 In the United States, pediatric patients with multiple chronic medical conditions are thought to account for 25% of acute care admissions, and 50% of hospital charges.7 Understanding the relative prevalence of different chronic diseases in children can have an important health policy, public health, and medical training implications.
Traditionally, epidemiological data on disease prevalence comes from claims billing data or from specially designed population surveys. However, electronic health records (EHRs) are becoming more prolific and the ability to aggregate data across EHRs is becoming easier. Therefore, the potential to use clinical data across multiple EHRs to study disease prevalence generally and chronic disease prevalence specifically, has increased. Because of the American Recovery and Reinvestment Act Health Information Technology for Economic and Clinical Health “Meaningful Use” program and other healthcare trends, over 80% of hospitals in the United States have implemented EHRs.8 In the US, the majority of pediatricians (>94%) now use an EHR to help deliver care.8,9,10 EHR implementation is expected to continue to grow in the future. In this study, we use aggregated EHR data from millions of pediatric patients to examine chronic disease prevalence and patient characteristics associated with different chronic pediatric conditions.
Methodology:
Database description
De-identified data were obtained using the Explore application of the Explorys platform (IBM Watson Health Explorys, Inc.). Explorys standardizes, normalizes, and aggregates EHR data from numerous healthcare systems across the United States, together accounting for over $69 billion in care, 360 hospitals, more than 317,000 providers, and more than 70 million patients.11 Explorys places a health data gateway (HDG) server behind the firewall of each participating healthcare organization. After collecting data from a variety of health information systems—EHRs, billing systems, laboratory systems, etc.—the HDG maps the data to standard informatics Unified Medical Language System (UMLS) ontologies (such as Systematized Nomenclature for Medicine– Clinical Terms (SNOMED-CT)) and standards and normalizes measurements as needed. Next, the data from each participating healthcare organization is passed into a data grid. A web application allows each healthcare organization to search and analyze the aggregated, standardized, normalized, and de-identified population-level data. The EHR serves as the primary medical record within participating institutions contributing to Explorys, which have many EHR vendors.
All data used was de-identified to meet Health Insurance Portability and Accountability Act (HIPAA) and Health Information Technology for Economic and Clinical Health (HITECH) Act standards. Business affiliation agreements were in place between all participating healthcare systems and Explorys regarding the contribution of EHR data and the use of these de-identified data. UMLS ontologies were used to map EHR data to facilitate searching and indexing. Diagnoses, findings, and procedures were mapped into the SNOMED-CT hierarchy. Many studies have been published using Explorys. Pfefferle et al. demonstrated that collecting the data through Explorys is very useful and accurate method to find out the link among large populations.12
Using the Explore IBM Watson Health Explorys module, we identified aggregated patient cohorts of eligible patients with the following chronic conditions of pediatric population documented as encounter diagnoses as part of primary care visits or the vital signs criteria (the criteria used for each of these diagnoses are provided in Appendix 1): asthma, attention deficit hyperactivity disorder (ADHD), autism, chromosomal disorders, diabetes, eczema, epilepsy, food allergies, obstructive sleep apnea (OSA), hypertension and obesity/overweight. Then we analyzed the data to determine chronic disease prevalence and patient characteristics associated with these diagnoses. All of the data, including demographic, vital sign, encounter diagnoses, and prescription data, used from Explorys in this study came from EHRs.
Patient population:
We included patients 17 years of age or younger (age was the age at the time of the data queries), who had at least one visit in which the provider specialty was a primary care specialty (Pediatrics, Family Medicine, or Internal Medicine-Pediatrics) between 2016 to 2018. Common pediatric chronic diseases were investigated. Based on the CDC definition of chronic disease1 we choose SNOMED-CT codes that by their very nature should only typically be used for conditions that would be chronic. We also analyzed the data based on patient age category (0–4, 5–9, 10–14, and 15–17), gender, race/ethnicity, and insurance. For hypertension and obesity/overweight we examined diagnoses based on vital sign measurement documented during patient encounters in the clinic, as opposed to relying only on SNOMED-CT encounter diagnosis codes, as described in Appendix 1.
Statistical analysis:
Statistical calculations were performed using Microsoft Excel 2016. The prevalence of each chronic condition was calculated and stratified by age category, gender, race/ethnicity, and insurance using the total number of patients in each stratified group (Column 2 of Table 2) as a denominator. Odds ratios were established by taking one subgroup as a reference and other subgroups were compared using the following formula: the rate of the event in an exposed/rate of the event in non-exposed. 95% confidence interval for each character was determined via formula; 95% CI = OR ± SE, [SE=1.96*SQRT(1/a + 1/b + 1/c + 1/d)].13
Table 2:
Demographic distribution of the pooled EHR Data for Children Aged 0–17 Years, 2016–2018 who visited primary care clinics (Pediatrics, Family Medicine or Internal Medicine Pediatrics providers).
| Criteria | Total Population | Obesity/ Overweight1 | Eczema2 | Asthma2 | Food Allergy2 | ADHD2 | HTN1 | OSA2 | Epilepsy2 | Autism2 | Diabetes2 | Chromosomal Disorder2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sample Size | 2662660 | 977301 | 419690 | 338320 | 124140 | 108940 | 108370 | 35220 | 28490 | 25200 | 24810 | 11100 |
| Age Category | ||||||||||||
| 0–4 | 30% (808370) | 9% (84770) | 19% (78630) | 11% (34670) | 20% (2455) | 1% (740) | 9% (9760) | 13% (4460) | 15% (4280) | 6% (1450) | 40% (9920) | 25% (2690) |
| 5–9 | 27% (731110) | 34% (339900) | 34% (143740) | 31% (104610) | 32% (41010) | 17% (22800) | 17% (19100) | 37% (13060) | 29% (8320) | 33% (8220) | 16% (4130) | 32% (3640) |
| 10–14 | 26% (702860) | 35% (351490) | 31% (132490) | 36% (124420) | 31% (38720) | 49% (55540) | 31% (33710) | 35% (12360) | 34% (9910) | 40% (10220) | 22% (5540) | 29% (3300) |
| 15–17 | 17% (454200) | 21% (215920) | 16% (69910) | 23% (79750) | 17% (21470) | 33% (32190) | 43% (47040) | 16% (5500) | 22% (6360) | 21% (5530) | 22% (5360) | 14% (1640) |
| Gender | ||||||||||||
| Female | 49% (1294860) | 48% (467510) | 47% (199120) | 41% (139950) | 45% (56170) | 31% (32830) | 44% (47830) | 44% (15390) | 46% (13050) | 80% (20100) | 49% (12170) | 47% (5330) |
| Male | 51% (1364190) | 52% (509800) | 53% (220560) | 59% (198360) | 55% (67960) | 69% (76100) | 56% (60540) | 57% (20040) | 54% (15440) | 20% (5100) | 51% (12620) | 53% (5880) |
| Race | ||||||||||||
| Caucasian | 66% (1762570) | 71% (692460) | 66% (276070) | 65% (218510) | 67% (82770) | 74% (80120) | 69% (74210) | 43% (15050) | 69% (1966) | 72% (18250) | 74% (18510) | 73% (8070) |
| African American | 13% (352280) | 15% (149540) | 19% (80740) | 21% (70420) | 18% (22710) | 16% (16610) | 19% (20800) | 16% (5650) | 17% (4860) | 13% (3360) | 9% (2200) | 11% (1200) |
| Unknown | 11% (284350) | 10% (98460) | 10% (43050) | 10% (33160) | 9% (10970) | 10% (10440) | 8% (8520) | 4% (1490) | 10% (2720) | 10% (2400) | 9% (2230) | 11% (1150) |
| Asian | 2% (59330) | 2% (17590) | 3% (12600) | 2% (6230) | 4% (4370) | 1% (750) | 1% (1310) | 1% (390) | 2% (520) | 2% (590) | 4% (1010) | 2% (250) |
| Multi-racial | 3% (71920) | 3% (32260) | 4% (15520) | 4% (13090) | 4% (4560) | 3% (3110) | 4% (4120) | 4% (1290) | 4% (1130) | 4% (980) | 4% (1010) | 3% (300) |
| Other | 8% (206560) | 9% (206560) | 7% (206560) | 9% (206560) | 6% (206560) | 6% (206560) | 10% (206560) | 32% (11350) | 10% (206560) | 7% (206560) | 13% (206560) | 10% (206560) |
| Ethnicity | ||||||||||||
| Non-Hispanic | 54% (1971320) | 80% (784230) | 79% (331340) | 79% (265470) | 83% (103410) | 82% (88790) | 83% (90030) | 51% (17880) | 80% (22880) | 81% (2850) | 70% (17260) | 14% (1540) |
| Hispanic | 30% (325460) | 14% (136150) | 12% (51210) | 14% (48740) | 9% (11250) | 10% (10340) | 13% (14560) | 8% (2690) | 14% (3860) | 11% (20550) | 28% (7020) | 79% (8740) |
| Unknown | 14% (365880) | 6% (56921) | 9% (37140) | 7% (24110) | 8% (9480) | 9% (9810) | 4% (3780) | 41% (14650) | 6% (1750) | 7% (1800) | 2% (530) | 7% (820) |
| Insurance | ||||||||||||
| Private/Commercial | 50% (1344980) | 43% (309990) | 55% (236200) | 54% (202040) | 51% (72310) | 56% (71170) | 35% (32210) | 51% (17790) | 47% (13410) | 41% (10170) | 45% (10290) | 42% (5360) |
| Medicaid | 25% (670630) | 22% (159930) | 28% (118610) | 33% (124060)) | 23% (32050) | 29% (35080) | 26% (24050) | 39% (13760) | 50% (14280) | 29% (7140) | 41% (9400) | 40% (5180) |
| Medicare | 0% (9210) | 1% (4870) | 0% (1800) | 0% (1680) | 0% (620) | 0% (580) | 1% (620) | 0 (150) | 0.1% (40) | 1% (130) | 0% (80) | 0% (50) |
| Other/Unknown | 24% (637840) | 33% (502511) | 17% (63080) | 13% (10540) | 74% (19160) | 15% (2110) | 38% (51490) | 0.1% (3520) | 2.7% (760) | 29% (7760) | 14% (5040) | 18% (510) |
No. of participants is based on sample. Percentages may not total 100 based on rounding. HTN – Hypertension ADHD – Attention Deficit Hyperactivity Disorder OSA – Obstructive Sleep Apnea 1 - by vital signs 2 – by SNOMED-CT
For HIPAA-compliant statistical de-identification purposes, the Explorys tool does not allow reporting on sample sizes less than 10, and all population counts above 10 are rounded to the nearest 10.
This study was deemed not to be human studies research by the institutional review board of the MetroHealth System because it involved only de-identified population-level data that did not include individual patient data as defined by HIPAA standards regarding the 18 types of information requiring special care.
Results:
Table 1 illustrates disease prevalence by age category, gender, race/ethnicity, and insurance. Table 2 reports summary statistics for the sample and the percentage demographic distribution of each chronic disease. A total of 2,662,660 pediatric patients were included in the final study sample. Of the total population, 49% were females and 51% were males, with relatively even distribution over the age ranges (0–4, 5–9, 10–14, and 15–17). 66% of participants were Caucasians, and 13% were African-Americans. Table 3 presents the odds ratio and confidence intervals for the individual characteristics for each chronic condition.
Table 1.
Prevalence of Most Common Chronic Conditions in Pediatric Population aged 0–17 Years, 2016–2018 who visited primary care clinics. Stratified by Age, Race, Sex, and Ethnicity.
| Criteria | Obesity/ Overweight1 | Eczema2 | Asthma2 | Food Allergy2 | ADHD2 | HTN1 | OSA2 | Epilepsy2 | Autism2 | Diabetes2 | Chromosomal Disorder2 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall | 36.7% | 15.8% | 12.7% | 4.7% | 4.09% | 4.07% | 1.3% | 1.1% | 0.95% | 0.9% | 0.4% |
| Age Category | |||||||||||
| 0–4 | 10.5% | 9.7% | 4.3% | 3.0% | 0.1% | 1.2% | 0.6% | 0.5% | 0.18% | 1.2% | 0.3% |
| 5–9 | 46.5% | 19.7% | 14.3% | 5.6% | 3.1% | 2.6% | 1.8% | 1.1% | 1.12% | 0.6% | 0.5% |
| 10–14 | 50.0% | 18.9% | 17.7% | 5.5% | 7.9% | 4.8% | 1.8% | 1.4% | 1.45% | 0.8% | 0.5% |
| 15–17 | 47.5% | 15.4% | 17.6% | 4.7% | 7.1% | 10.4% | 1.2% | 1.4% | 1.22% | 1.2% | 0.4% |
| Gender | |||||||||||
| Female | 36.1% | 15.4% | 10.8% | 4.3% | 2.5% | 3.7% | 1.2% | 1.0% | 1.47% | 0.9% | 0.4% |
| Male | 37.4% | 16.2% | 14.5% | 4.9% | 5.6% | 4.4% | 1.5% | 1.1% | 0.39% | 0.9% | 0.4% |
| Race | |||||||||||
| Caucasian | 39.3% | 15.7% | 12.4% | 4.7% | 4.5% | 4.2% | 0.9% | 1.1% | 1.04% | 1.1% | 0.5% |
| African American | 42.4% | 22.9% | 20.0% | 6.4% | 4.7% | 5.9% | 1.6% | 1.4% | 0.95% | 0.6% | 0.3% |
| Unknown | 34.6% | 15.1% | 11.7% | 3.9% | 3.7% | 3.0% | 0.5% | 1.0% | 0.84% | 0.8% | 0.4% |
| Asian | 29.6% | 21.2% | 10.5% | 7.4% | 1.3% | 2.2% | 0.7% | 0.9% | 0.99% | 1.7% | 0.4% |
| Multi-Racial | 44.9% | 21.6% | 18.2% | 6.3% | 4.3% | 5.7% | 1.8% | 1.6% | 1.36% | 1.4% | 0.4% |
| Other | 42.6% | 14.4% | 14.5% | 3.8% | 3.1% | 5.0% | 8.6% | 1.3% | 0.8% | 1.5% | 0.5% |
| Ethnicity | |||||||||||
| Non-Hispanic | 39.8% | 16.8% | 13.5% | 5.3% | 4.5% | 4.6% | 0.9% | 1.2% | 0.88% | 0.9% | 0.1% |
| Hispanic | 41.8% | 15.7% | 15.0% | 3.5% | 3.2% | 4.5% | 0.8% | 1.2% | 1.04% | 2.2% | 2.7% |
| Unknown | 15.6% | 10.2% | 6.6% | 2.6% | 2.7% | 1.0% | 4.0% | 0.5% | 0.5% | 0.1% | 0.2% |
| Insurance | |||||||||||
| Private/Commercial | 31.7% | 56.3% | 59.7% | 58.2% | 65.3% | 29.7% | 1.3% | 48.5% | 40.4% | 41.5% | 48.3% |
| Medicaid | 16.4% | 28.3% | 36.7% | 25.8% | 32.2% | 22.2% | 2.1% | 51.2% | 28.3% | 37.9% | 46.7% |
| Medicare | 0.5% | 0.4% | 0.5% | 0.5% | 0.5% | 0.6% | 1.6% | 0.1% | 0.5% | 0.3% | 0.5% |
| Other/Unknown | 51.4% | 15.0% | 3.1% | 15.4% | 1.9% | 47.5% | 0.6% | 0.2% | 30.8% | 20.3% | 4.6% |
HTN – Hypertension ADHD – Attention Deficit Hyperactivity Disorder OSA – Obstructive Sleep Apnea 1 - by vital signs 2 – by SNOMED-CT
Table 3.
Odds ratio with 95% confidence interval.
| Characteristic | Obesity/ Overweight1 | Eczema2 | Asthma2, 3 | Food Allergy2, 3 | ADHD2 |
HTN1 | OSA2 | Epilepsy2 | Autism2 | Diabetes2, 3 | Chromosomal Disorder2 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Age Category | |||||||||||
| 0–4 | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
| 5–9 | 4.43 (4.42–4.44) | 2.02 (2.01–2.03) | 3.3 (3.3–3.4) | 1.9 (1.8–1.9) | 34.07 (33.99–34.14) | 2.16 (2.14–2.19) | 3.24 (3.20 – 3.27) | 2.02 (2.01–2.03) | 6.27 (6.21–6.32) | 0.5 (0.4–0.5) | 1.50 (1.45–1.55) |
| 10–14 | 4.77 (4.76–4.78) | 1.94 (1.93–1.95) | 4.13 (4.1–4.1) | 1.8 (1.8–1.8) | 86.32 (86.25–86.39) | 3.97 (3.95–4.00) | 3.19 (3.15 – 3.22) | 1.94 (1.93–1.95) | 8.11 (8.05–8.16) | 0.6 (0.6–0.7) | 1.41 (1.36–1.46) |
| 15–17 | 4.53 (4.52–4.54) | 1.58 (1.57–1.59) | 4.09 (4.1–4.1) | 1.6 (1.5–1.6) | 77.42 (77.35–77.49) | 8.58 (8.56–8.60) | 2.19 (2.16 – 2.23) | 1.58 (1.57–1.59) | 6.79 (6.73–6.85) | 1.0 (0.9–1.0) | 1.09 (1.02–1.15) |
| Gender | |||||||||||
| Female | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
| Male | 1.04 (1.03–1.04) | 1.05 (1.04–1.06) | 1.4 (1.3–1.4) | 1.2 (1.1–1.2) | 2.20 (2.19–2.21) | 1.20 (1.19–1.21) | 1.23 (1.21 – 1.26) | 1.05 (1.04–1.06) | 3.74 (3.71–3.77) | 1.0 (1.0–1.0) | 1.07 (1.03–1.11) |
| Race | |||||||||||
| Caucasian | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
| African American | 1.08 (1.07–1.09) | 1.46 (1.45–1.47) | 1.6 (1.6–1.6) | 1.4 (1.4–1.4) | 1.04 (1.02–1.05) | 1.40 (1.39–1.42) | 1.88 (1.85 – 1.91) | 1.46 (1.45–1.47) | 0.92 (0.88–0.96) | 0.6 (0.6–0.6) | 0.74 (0.68–0.80) |
| Unknown | 0.88 (0.87–0.89) | 0.97 (0.96–0.98) | 0.9 (0.9–1.0) | 0.8 (0.8–0.8) | 0.81 (0.79–0.83) | 0.71 (0.69–0.73) | 0.61 (0.56 – 0.67) | 0.97 (0.96–0.98) | 0.82 (0.77–0.86) | 0.8 (0.7–0.8) | 0.88 (0.82–0.95) |
| Asian | 0.75 (0.74–0.77) | 1.36 (1.34–1.38) | 0.9 (0.8–0.9) | 1.6 (1.5–1.6) | 0.28 (0.21–0.35) | 0.52 (0.47–0.58) | 0.77 (0.67 – 0.87) | 1.36 (1.34–1.38) | 0.96 (0.88–1.04) | 1.6 (1.6–1.7) | 0.92 (0.79–1.05) |
| Multi-Racial | 1.14 (1.13–1.16) | 1.38 (1.36–1.40) | 1.5 (1.4–1.5) | 1.4 (1.4–1.4) | 0.95 (0.91–0.99) | 1.36 (1.33–1.39) | 2.10 (2.04 – 2.16) | 1.38 (1.36–1.40) | 1.32 (1.25–1.38) | 1.3 (1.3–1.4) | 0.91 (0.80–1.03) |
| Other | 1.08 (1.07–1.09) | 0.92 (0.92–0.95) | 1.17 (1.15–1.20) | 0.82 (0.79–0.84) | 0.67 (0.65–0.70) | 1.20 (1.18–1.22) | 10.13 (10.10 – 10.15) | 1.18 (1.14–1.22) | 0.81 (0.76–0.86) | 1.47 (1.43–1.50) | 1.15 (1.09–1.22) |
| Ethnicity | |||||||||||
| Non-Hispanic | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
| Hispanic | 1.05 (1.04–1.06) | 0.94 (0.93–0.95) | 1.1 (1.1–1.1) | 0.7 (0.6–0.7) | 0.71 (0.68–0.73) | 0.98 (0.96–1.00) | 0.91 (0.87 – 0.95) | 1.07 (1.06–1.08) | 0.84 (0.80–0.88) | 2.5 (2.4–2.5) | 34.38 (34.32–34.43) |
| Unknown | 0.39 (0.38–0.40) | 0.39 (0.38–0.41) | 0.15 (0.13–0.18) | 0.49 (0.47–0.52) | 0.60 (0.57–0.62) | 0.23 (0.19–0.26) | 4.41 (4.39 – 4.44) | 0.41 (0.36–0.46) | 0.47 (0.42–0.52) | 0.17 (0.08–0.25) | 2.87 (2.78–2.95) |
| Insurance | |||||||||||
| Private/Commercial | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
| Medicaid | 1.03 (1.03–1.04) | 1.01 (1.00–1.01) | 1.23 (1.22–1.24) | 0.89 (0.88–0.90) | 0.98 (0.98–1) | 1.50 (1.48–1.51) | 1.55 (1.53 – 1.57) | 2.12 (2.09–2.14) | 1.41 (1.38–1.44) | 1.83 (1.80–1.86) | 2.61 (2.5–2.65) |
| Medicare | 2.29 (2.26–2.33) | 1.11 (1.06–1.16) | 1.21 (1.16–1.27) | 1.25 (1.17–1.33) | 1.19 (1.11–1.27) | 2.81 (2.73–2.89) | 1.23 (1.07 – 1.39) | 0.42 (0.11–0.73) | 1.87 (1.69–2.04) | 1.14 (0.91–1.36) | 1.31 (1.03–1.58) |
| Other/Unknown | 3.42 (3.41–3.42) | 0.56 (0.55–0.57) | 0.11 (0.09–0.13) | 0.56 (0.54–0.57) | 0.06 (0.02–0.11) | 3.37 (3.36–3.39) | 0.42 (0.38 – 0.45) | 0.12 (0.05–0.19) | 1.61 (1.58–1.64) | 1.03 (1.0–1.07) | 0.15 (0.06–0.24) |
HTN – Hypertension ADHD – Attention Deficit Hyperactivity Disorder OSA – Obstructive Sleep Apnea 1 - by vital signs 2 – by SNOMED-CT 3 – values in column rounded to nearest decimal point if rounding did not impact odd radio or confidence interval including 1.0.
Italics indicates confidence internal includes 1.0.
The most prevalent chronic disease identified was obesity/overweight by vital signs, followed by eczema diagnoses. Older children had a greater prevalence of obesity/overweight compared to younger children. Males were more likely to be obese/overweight. Multiracial and Hispanic patients had higher odds to be obese/overweight, and so were patients with Medicaid, Medicare, and Other/Unknown insurance compared to patients with private/commercial insurance.
The overall prevalence of eczema diagnoses was 15.8%, slightly higher in males than females. Prevalence was highest in the 5–9-year age group, among African American race and non-Hispanic ethnicity children, and children with Medicaid or Medicare insurance.
The prevalence of asthma diagnoses across the entire sample was 12.7%, higher in males than females. The highest prevalence of asthma was in children aged 10 – 14 years. Children and adolescents in the older age groups had higher odds of having asthma than children aged 0–4 years; African-Americans were more likely to have asthma diagnoses than Caucasians, as were Hispanics compared to non-Hispanics, and patients with Medicaid and Medicare insurance compared to private/commercial insurance.
The prevalence of food allergy diagnoses across the sample was 4.7%, slightly higher in males, and peaked in the 5–9 age group. African-Americans compared to Caucasians, and Hispanics compared to non-Hispanics, as well as patients on Medicare insurance compared to private/commercial insurance were all more likely to have a food allergy diagnosis.
We also determined the prevalence of ADHD diagnoses was 4.09%. The age group with the highest prevalence for ADHD and autism was 10–14. ADHD was more prevalent in males and African-American race. ADHD was more common among non-Hispanics compare to Hispanics, as well as patients with Medicaid and Medicare.
The prevalence of hypertension using incident vital signs documented during patient encounters in clinic visits was 4.07%, with higher rates among older children. The prevalence of hypertensive blood pressure was greater in males compares to females, as well as more likely in African-Americans compared to Caucasians, non-Hispanics compared to Hispanics, and patients with Medicaid or Medicare insurance compared to private/commercial insurance. The prevalence of hypertension determined by including only patients with a diagnosis of hypertension was 0.1% overall but showed similar age and gender trends.
In our study, 35,220 children have a diagnosis of OSA, yielding a prevalence of 1.3%. This study showed that the risk of having an OSA diagnoses decreases with age. The highest risk is in the 5–9 years age group. OSA diagnoses were also more common in males and those with Medicaid insurance.
The prevalence of epilepsy diagnoses across the sample was 1.1%. Epilepsy diagnoses increased significantly with age category and were slightly more likely in males. African-American race compared to Caucasian, Hispanic ethnicity compared to non-Hispanic, and patients with Medicare insurance were all associated with higher epilepsy diagnoses.
The prevalence of diabetes diagnoses (types 1 (DM1) and 2 (DM2) combined) across the entire sample was 0.9%; DM1 and DM2 were 0.7% and 0.3% respectively. There was no significant difference in gender for diabetes. Asian children were more likely to have a diabetes diagnoses compared to Caucasians. Additionally, Hispanics ethnicity, and Medicaid or Medicare insurance was correlated with a diabetes diagnosis.
The prevalence of autism and chromosomal disorder diagnoses were 0.95% and 0.42% respectively. The age group with the highest prevalence for autism was 10–14, while that for chromosomal disorders was 5–9. The odds ratio for male prevalence in comparison to females was higher in both groups. Caucasian compared to African-Americans, as well as patients with Medicaid or Medicare insurance, were more likely to have both diagnoses. Hispanics compared to non-Hispanics were less likely to have a diagnosis of autism, but more like to have a chromosomal disorder diagnosis.
Discussion:
For the first time, we used aggregated EHR data from many different healthcare systems to investigate chronic disease prevalence in over 2 million pediatric patients. Overall, this study identified the top 6 chronic pediatric conditions as obesity/overweight (36.7%), eczema (15.8%), asthma (12.7%), food allergy (4.7%), ADHD (4.09%) and hypertension (4.07%). Most of these chronic conditions were more prevalent in relatively older age pediatric patients, males, and African-Americans, and multiracial population.
Although overweight/obesity meets the CDC criteria for the chronic disease in pediatric patients,1 it is the most common undiagnosed, underdiagnosed, and underappreciated chronic disease in the pediatric population. The overall prevalence of obesity/overweight in our study is 36.7% which is quite comparable to that in NHANES (34%) and Multi-Institutional EHR data (35%).14 Previous studies have noted the prevalence of childhood obesity in the US to be 17.8% (95% CI 16.1%−19.6%) compared to our 17.1%.15 Table 4 further illustrates the comparison of overweight and obesity of our study with that of NHANES and Bailey et al. Previous data has shown that although clear BMI percentile definitions of pediatric weight problems exist, a large percentage of overweight and obese patients remain undiagnosed.16 The percentage of participants with a diagnosis of obesity/overweight in that study was only 2.5% while that in our study is 3.13%, even though a single abnormal BMI percentile measure is sufficient for an overweight or obesity diagnosis, and the vast majority of the pediatric patient with a single abnormal BMI percentile continues to have abnormal BMI percentiles for a year or more. Hence our findings reaffirm the underdiagnosis of obesity (and overweight) in children and adolescents and also show why reviewing claims data alone is not sufficient to look at chronic disease prevalence.
Table 4.
Prevalence of Obesity and Overweight in EHR-Derived Data and NHANES Data.
| Fraction of sampleb | Prevalence percentage of obesity | Prevalence percentage of overweight | |
|---|---|---|---|
| NHANES 2007–08 a,14 | |||
| 2–17 years | 1.000 | 18 | 16 |
| 2–4 years | 0.194 | 11 | 12 |
| 5–10 years | 0.349 | 19 | 15 |
| 11–17 years | 0.457 | 20 | 17 |
| NHANES 2015–16 17 | |||
| 2–19 | 1.000 | 18.5 | 16.6 |
| 2–5 | -- | 13.9 | -- |
| 6–11 | -- | 18.4 | -- |
| 12–19 | -- | 20.6 | -- |
| Multi-site EHR Data14 (2012) | |||
| 2–17 years | 1.000 | 18 | 17 |
| 2–4 years | 0.280 | 14 | 16 |
| 5–10 years | 0.418 | 18 | 17 |
| 11–17 years | 0.374 | 20 | 17 |
| Explorys (2016–18) | |||
| 0–17 years | 1.000 | 17.1 | 16.4 |
| 0–4 years | 0.304c | 5.2 | 4.1 |
| 5–10 years | 0.276c | 22.9 | 20.6 |
| 11–15 years | 0.264c | 22.4 | 22.7 |
| 15–17 years | 0.171c | 20.6 | 21.7 |
Overweight is body mass index (BMI) at or above the 85th percentile and below the 95th percentile from the sex-specific BMI-for-age 2000 CDC Growth Charts. Obesity is BMI at or above the 95th percentile.
All proportions for NHANES data were calculated using MEC sample weights; no BMI outliers were excluded in prevalence estimates following NHANES standard practice.
Total raw samples sizes were 3032 for NHANES 2007–08, 3340 for NHANES 2015–16, 528,340 for Bailey et al. and 2,662,660 for Explorys.
No. of participants is based on sample. Percentages may not total 100 based on rounding.
NHANES: National Health and Nutrition Examination Survey.
Studies have shown that the prevalence of obesity may vary depending on racial/ethnic characteristics. In African-American or Hispanic children, obesity is currently two to four times greater in comparison to their Caucasian counterparts.17,18 We also found that multiracial and African-American groups compared to Caucasians, and Hispanics compared to non-Hispanics were more likely to be obese/overweight.
In our analysis, eczema was the second most prevalent chronic disease in children under 18 years with a prevalence of 15.8%. This prevalence is higher than that calculated by Tatyana E. Shaw (10.7%).19 Another study showed that eczema prevalence among Hispanic and non-Hispanic Caucasian children showed that the eczema prevalence of Hispanic Caucasian children was lower compared to non-Hispanic Caucasians, and sociodemographic differences were not responsible for this ethnic difference.20 The findings of their study are consistent with our findings, which show that non-Hispanics have greater odds of having eczema than Hispanics.
Asthma was the third most common diagnosis in our cohort, afflicting 12.7% of the population. The US Centers for Disease Control and Prevention reports an asthma prevalence of 8.3%.21 One reason for this difference may be that the CDC data is based on survey data from the National Health Interview Survey and so would include healthy children not seen recently in a primary care healthcare setting and so would be expected to have a higher denominator (and therefore lower percentage) reported than through our methodology of pooled EHR data which only looks at patients seeking care within a healthcare system (with an EHR). In terms of food allergy prevalence, according to the metanalysis by Rona et al. the prevalence of self-reported food allergy to any food ranges from 3% to 35%,22 a range that our results also fall in. Gupta et al. reported that food allergy is more common in males, which our analysis also showed.23
Our study reported the prevalence of ADHD to be 4.09%. The estimates of the percentage of children who have ADHD have changed over time. Table 5 demonstrates the comparison of different studies on prevalence of ADHD in the US children and adolescents. The prevalence rages from 2.2% to 9.9%, and our finding falls in this range.24–29
Table 5:
Prevalence of ADHD in US children and adolescents, comparing different studies.
| Authors | Population | Method | Informant | Age range | Sample size | Prevalence percentage |
|---|---|---|---|---|---|---|
| Simonoff et al.24 | White twins | Interview (subject, parent) Rating scale (subject, parent, teacher) | Subject, parent, teacher | 8–16 | 5524 (2762 pairs) |
2.4% |
| Shaffer et al.25 | Sample of children aged 9–17 (details not given) | Interview | Parent, subject | 9–17 | 1285 | 4.5% (parent) 2.2% (subject) |
| Wolraich et al.26 | School | Rating scale | Teacher | 4–12 | 8258 | 7.3% |
| Danialson et al.27 | 2016 National Survey of Children’s Health (NSCH) | Interview | Parents | 2–17 | 45736 | 8.4% |
| August and Garfinkel28 | School | Rating scale | Teacher | 5–14 | 1038 | 8.6% |
| Hudziak et al.29 | Female twin in community | Interview | Parent | 13.5–19.5 | 2538 (1269 pairs) |
9.9% |
| Mean Prevalence | 6.2% | |||||
| Mean Weighted Prevalence | 7.6% |
In terms of hypertension, as reported by several studies30–33, the prevalence percentage of hypertension in pediatric patients ranges from 1% to 5%, a range that our findings of 4.07% also fall in. According to the current literature reviews, the prevalence range of OSA in the general pediatric population is 0%−5.7%, and our finding (1.3%) is in that range.34,35 For epilepsy, a metanalysis found the point prevalence range of active epilepsy to be between 3.90–9.99/1000 (0.4% - 1%), which is close to our results (1.1%).36 The cumulative prevalence of all autism is 4.0 cases in 1000 children (0.4%) and was found to be more prevalent in males.37,38 These findings are similar to our analysis. In terms of diabetes, based on CDC data, the crude prevalence of diabetes mellitus in children is estimated at 0.7% similar to our 0.9%. Similar to our study the CDC, despite the significant increase in DM2 because of the obesity epidemic, DM1 still has a higher prevalence than DM2 in the pediatric population by approximately 2:1.39 For chromosomal abnormalities, our finding of 0.4% prevalence is consistent with other studies showing approximately 4 per 1,000 births and 43.8 per 10,000 births.40,41
According to one study done in Southeastern Michigan on 14,404 pediatric patients, the prevalence of chronic conditions rose from 9.8% to 13.8% in the pediatric population from 2009 to 2013.42 This study does not include obesity/overweight as a chronic condition and so when obesity/overweight is taken out of the current study, presents a similar estimate that 15–20% of the pediatric population has at least one (non-obesity/overweight) chronic condition. The emotional, physical, and social development of a child may affect chronic conditions and often have lasting consequences on health.42,43 In one study, it was illustrated that 43% of US pediatrics population have one of the 20 chronic diseases. It increases to 54.1% if we include overweight, obesity or being at risk for developmental delays. Out of these, 19.2% need special health care.44 Therefore, understanding the overall chronic disease prevalence by disease type and by patient characteristics is important to better understanding pediatric chronic diseases as this will aid us in taking steps and making policies that will help us in preventing and/or more effectively addresses chronic diseases.
The data analysis for this research was performed over an 11-week period using existing pooled, de-identified data of approximately 2.6 million patients from EHR data. This approach relies on the underlying data infrastructure collected by thousands of clinicians as the normal part of clinical care. Using de-identified data, we were able to estimate the prevalence of chronic conditions in childhood from a large cohort of patients, and the inclusion of this large sample from multiple institutions is the main strength of our data.
This study and the underlying approach have limitations. This study demonstrates both the potential and limitations of this type of clinical research informatics tools and pooled, de-identified, clinical data from multiple healthcare systems using different EHRs. First of all, in order to determine if someone had a chronic condition, we relied on structured data (diagnosis codes or vital signs) in the EHR. Unstructured data, such as signs/symptoms, investigations or diagnoses, written into notes, could not be used as these were not coded in structured digital data/ontologies. Although, multiple encounters could have had the suitable data, we could not look for repeated occurrences either by the same PCP, different PCPs, specialists, etc. For statistical de-identification, the researchers were only able to report on counts to the nearest 10. However, given the large sample size, this limitation should not have had any significant effect on the qualitative or even quantitative findings of the study. Additionally, the completeness of data and length of time of people in the cohort of the study was variable. We tried to address this by making sure that each patient had at least one visit with a provider in a primary care specialty during the study period, with the assumption that a primary care provider should have and document a complete picture of a patients’ chronic conditions. Moreover, the database application used for acquiring records of all patients, Expolrys, is limited by its ability to keep sequential records of hypertension. Finally, the clinical data was initially obtained for clinical and not specifically research purposes and so the accuracy may be less than data collected solely for research purposes. However, in our experience, vital sign data typically have less than a 1% error rate.45 In spite of this limitation, qualitatively and quantitatively results of this study generally match those reported elsewhere.
Conclusion:
Our study concludes that the top six chronic conditions in the US pediatric population, who seeks care in healthcare systems with an EHR, are obesity/overweight, eczema, asthma, food allergy, ADHD, and hypertension, in descending order. Most of the chronic conditions are more prevalent among the relatively older pediatric age group, African Americans, and multiracial population groups. Also, this study demonstrates that aggregated, de-identified EHR data from numerous different EHRs can be used calculate chronic disease prevalence and odds ratio of various risk factors.
What’s New.
This study uses pooled electronic health record data to determine the prevalence of common pediatric chronic diseases and examines patient demographic characteristics associated with these conditions.
Funding/Support:
No funding/support was secured for this study
Abbreviations:
- ADHD
Attention Deficit Hyperactive Disorder
- ASD
Autism Spectrum Disorder
- BMI
Body Mass Index
- CDC
Centers for Disease Control and Prevention
- DM1
Diabetes Mellitus Type 1
- DM2
Diabetes Mellitus Type 2
- DSM-5
Diagnostic and Statistical Manual of Mental Disorders
- EHRs
Electronic Health Records
- HDG
Health Data Gateway
- HDG
Health Data Gateway
- HITECH
Health Information Technology for Economic and Clinical Health Act
- HIPAA
Health Insurance Portability and Accountability Act
- ICD
International Classification of Diseases
- MCC
Multiple Chronic Conditions
- NHANES
National Health and Nutrition Examination Survey
- OSA
Obstructive Sleep Apnea
- SNOMED-CT
Systematized Nomenclature for Medicine – Clinical Terms
- UMLS
Unified Medical Language System
Appendix 1:
The criteria used to define each condition in IBM Watson Health Explorys.
| Condition | Diagnoses Criteria (SNOMED-CT encounter diagnoses codes) | Vital Sign Criteria |
|---|---|---|
| Obesity/Overweight | Obesity OR Morbid obesity OR Overweight |
At least one BMI percentile: Obese
[>=95] (body mass index
percentile) OR Overweight [85–94] (body mass index percentile) |
| Eczema | Eczema | n/a |
| Asthma | Asthma OR Asthma without status asthmaticus |
n/a |
| Food Allergy | Food Allergy | n/a |
| Attention Deficit Hyperactive Disorder | Attention deficit hyperactivity disorder | n/a |
| Hypertension | Benign
Hypertension OR Renovascular hypertension OR Neonatal hypertension OR Venous hypertension OR Renal hypertension OR Secondary hypertension OR Portal hypertension |
At least one Stage 1 hypertension
reading [systolic 140–159 diastolic 90–99] (blood
pressure) OR At least one Stage 2 hypertension [systolic >=160 diastolic >=100] (blood pressure) |
| Obstructive Sleep Apnea | Obstructive Sleep Apnea | n/a |
| Epilepsy | Epilepsy OR Seizures disorder |
n/a |
| Diabetes | Diabetes Miletus type
1 OR Diabetes Miletus type 2 OR Diabetes Miletus |
n/a |
| Autism | Residual infantile
autism OR Active infantile autism OR Infantile autism |
n/a |
| Chromosomal Disorders | Chromosomal Disorders | n/a |
n/a – not applicable
Footnotes
Financial Disclosure: Authors have no financial sponsorship relevant to this article to disclose
Conflict of Interest Disclosure: Authors have no conflicts of interest to divulge
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